scholarly journals Response of freeze-thaw processes to experimental warming in the permafrost regions of the central Qinghai-Tibet Plateau

2016 ◽  
Author(s):  
Shengyun Chen ◽  
Wenjie Liu ◽  
Qian Zhao ◽  
Lin Zhao ◽  
Qingbai Wu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Content ◽  
Tibet Plateau ◽  
Active Layer Thickness ◽  
Experimental Warming ◽  
Freeze Thaw ◽  
Alpine Steppe ◽  
Steppe Ecosystems ◽  
Qinghai Tibet Plateau ◽  
Permafrost Regions

Abstract. Assessing quantitatively effect of climate warming on freeze/thaw index (FI/TI), soil freeze-thaw processes and active layer thickness (ALT) is still lacking in the permafrost regions of the Qinghai-Tibet Plateau (QTP) until now. Experimental warming was manipulated using open top chambers (OTCs) in alpine swamp meadow and alpine steppe ecosystems in the permafrost regions of the central QTP during 2009–2011. Under OTCs treatment, air temperature (Ta) significantly increased in the daytime and decreased in the nighttime, diurnal and annual Ta range significantly enhanced, and mean annual Ta increased by 1.4 °C. Owing to the experimental warming, mean annual soil temperature at the depths from 5 cm to 40 cm was increased by 0.2 ~ 0.7 °C in alpine swamp meadow and 0.3 ~ 1.5 °C in alpine steppe. Mean annual soil moisture content at 10 cm depth decreased by 1.1 % and 0.8 %, and mean annual soil salinity at 10 cm depth significantly increased by 0.3 g L-1 and 0.1 g L-1 in alpine swamp meadow and alpine steppe, respectively. Further, FI was significantly decreased by 410.7 °C d while TI was significantly increased by 460.7 °C d. Likewise, the onset dates of shallow soil thawing at 5–40 cm depths were advanced by 9 days and 8 days while the onset dates of freezing were delayed by 10 days and 4 days in alpine swamp meadow and alpine steppe, respectively. Moreover, soil frozen days were significantly decreased by 28 days and 16 days, but thawed days were increased by 18 days and 6 days, and frozen-thawed days were significantly increased by 10 days and 10 days in alpine swamp meadow and alpine steppe, respectively. Furthermore, ALT would be significantly increased by ~ 6.9 cm and ~ 19.6 cm in alpine swamp meadow and alpine steppe ecosystems, respectively.

scholarly journals Soil Moisture Calibration Equations for Active Layer GPR Detection—a Case Study Specially for the Qinghai–Tibet Plateau Permafrost Regions

2020 ◽  
Vol 12 (4) ◽  
pp. 605
Author(s):  
Erji Du ◽  
Lin Zhao ◽  
Defu Zou ◽  
Ren Li ◽  
Zhiwei Wang ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Soil Moisture ◽  
Active Layer ◽  
Complex Refractive Index ◽  
Tibet Plateau ◽  
Data Set ◽  
Calibration Equation ◽  
Qinghai Tibet Plateau ◽  
Moisture Estimation ◽  
Permafrost Regions

Ground-penetrating radar (GPR) is a convenient geophysical technique for active-layer soil moisture detection in permafrost regions, which is theoretically based on the petrophysical relationship between soil moisture (θ) and the soil dielectric constant (ε). The θ–ε relationship varies with soil type and thus must be calibrated for a specific region or soil type. At present, there is lack of such a relationship for active-layer soil moisture estimation for the Qinghai–Tibet plateau permafrost regions. In this paper, we utilize the Complex Refractive Index Model to establish such a calibration equation that is suitable for active-layer soil moisture estimation with GPR velocity. Based on the relationship between liquid water, temperature, and salinity, the soil water dielectric constant was determined, which varied from 84 to 88, with an average value of 86 within the active layer for our research regions. Based on the calculated soil-water dielectric constant variation range, and the exponent value range within the Complex Refractive Index Model, the exponent value was determined as 0.26 with our field-investigated active-layer soil moisture and dielectric data set. By neglecting the influence of the soil matrix dielectric constant and soil porosity variations on soil moisture estimation at the regional scale, a simple active-layer soil moisture calibration curve, named CRIM, which is suitable for the Qinghai–Tibet plateau permafrost regions, was established. The main shortage of the CRIM calibration equation is that its calculated soil-moisture error will gradually increase with a decreasing GPR velocity and an increasing GPR velocity interpretation error. To avoid this shortage, a direct linear fitting calibration equation, named as υ-fitting, was acquired based on the statistical relationship between the active-layer soil moisture and GPR velocity with our field-investigated data set. When the GPR velocity interpretation error is within ±0.004 m/ns, the maximum moisture error calculated by CRIM is within 0.08 m3/m3. While when the GPR velocity interpretation error is larger than ±0.004 m/ns, a piecewise formula calculation method, combined with the υ-fitting equation when the GPR velocity is lower than 0.07 m/ns and the CRIM equation when the GPR velocity is larger than 0.07 m/ns, was recommended for the active-layer moisture estimation with GPR detection in the Qinghai–Tibet plateau permafrost regions.

scholarly journals Consumption of atmospheric methane by the Qinghai–Tibet Plateau alpine steppe ecosystem

2018 ◽  
Vol 12 (9) ◽  
pp. 2803-2819 ◽  
Author(s):  
Hanbo Yun ◽  
Qingbai Wu ◽  
Qianlai Zhuang ◽  
Anping Chen ◽  
Tong Yu ◽  
...  
Keyword(s):  
Soil Temperature ◽  
The Arctic ◽  
Tibet Plateau ◽  
Active Layer Thickness ◽  
Permafrost Region ◽  
Freezing And Thawing ◽  
Alpine Steppe ◽  
Environmental Controls ◽  
Steppe Ecosystem ◽  
Qinghai Tibet Plateau

Abstract. The methane (CH4) cycle on the Qinghai–Tibet Plateau (QTP), the world's largest high-elevation permafrost region, is sensitive to climate change and subsequent freezing and thawing dynamics. Yet, its magnitudes, patterns, and environmental controls are still poorly understood. Here, we report results from five continuous year-round CH4 observations from a typical alpine steppe ecosystem in the QTP permafrost region. Our results suggest that the QTP permafrost region was a CH4 sink of -0.86±0.23 g CH4-C m−2 yr−1 over 2012–2016, a rate higher than that of many other permafrost areas, such as the Arctic tundra in northern Greenland, Alaska, and western Siberia. Soil temperature and soil water content were dominant factors controlling CH4 fluxes; however, their correlations changed with soil depths due to freezing and thawing dynamics. This region was a net CH4 sink in autumn, but a net source in spring, despite both seasons experiencing similar top soil thawing and freezing dynamics. The opposite CH4 source–sink function in spring versus in autumn was likely caused by the respective seasons' specialized freezing and thawing processes, which modified the vertical distribution of soil layers that are highly mixed in autumn, but not in spring. Furthermore, the traditional definition of four seasons failed to capture the pattern of the annual CH4 cycle. We developed a new seasonal division method based on soil temperature, bacterial activity, and permafrost active layer thickness, which significantly improved the modeling of the annual CH4 cycle. Collectively, our findings highlight the critical role of fine-scale climate freezing and thawing dynamics in driving permafrost CH4 dynamics, which needs to be better monitored and modeled in Earth system models.

scholarly journals ACTIVE LAYER THICKNESS RETRIEVAL OVER THE QINGHAI-TIBET PLATEAU FROM 2000 TO 2020 BASED ON INSAR-MEASURED SUBSIDENCE AND MULTI-LAYER SOIL MOISTURE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 437-442
Author(s):  
T. Chang ◽  
J. Han ◽  
Z. Li ◽  
Y. Wen ◽  
T. Hao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Layer Thickness ◽  
Active Layer ◽  
Surface Deformation ◽  
Tibet Plateau ◽  
Active Layer Thickness ◽  
Permafrost Region ◽  
Radar Images ◽  
Soil Moisture Data ◽  
Qinghai Tibet Plateau

Abstract. Active layer thickness (ALT) is an important index to reflect the stability of permafrost. The retrieval of ALT based on Interferometric Synthetic Aperture Radar (InSAR) technology has been investigated recently in permafrost research. However, most of such studies are carried out in a limited extend and relatively short temporal coverage. The combination of temporal-spatial multi-layer soil moisture data and multi-temporal InSAR is a promising approach for the large-scale characterization of ALT. In this study, we employed Small Baseline Subset Interferometry (SBAS-InSAR) technology to obtain the seasonal surface deformation from radar images of Envisat and Sentinel-1 in a permafrost region of Qinghai-Tibet Plateau (QTP). We attempt to verify and calibrate the temporal-spatial multi-layer soil moisture product in combination with the in-situ data. Based on the land subsidence data and the temporal-spatial multi-layer soil moisture data, we further improve method to retrieve the ALT information. This paper describes the progress so far and point out the future work.

scholarly journals Three in one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai–Tibet Plateau

2021 ◽  
Vol 15 (6) ◽  
pp. 3021-3033
Author(s):  
Jiahua Zhang ◽  
Lin Liu ◽  
Lei Su ◽  
Tao Che
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Active Layer ◽  
Snow Depth ◽  
Soil Moisture Content ◽  
Ground Surface ◽  
Surface Elevation ◽  
Tibet Plateau ◽  
Elevation Changes ◽  
Qinghai Tibet Plateau

Abstract. Ground surface elevation changes, soil moisture, and snow depth are all essential variables for studying the dynamics of the active layer and permafrost. GPS interferometric reflectometry (GPS-IR) has been used to measure surface elevation changes and snow depth in permafrost areas. However, its applicability to estimating soil moisture in permafrost regions has not been assessed. Moreover, these variables were usually measured separately at different sites. Integrating their estimates at one site facilitates the comprehensive utilization of GPS-IR in permafrost studies. In this study, we run simulations to elucidate that the commonly used GPS-IR algorithm for estimating soil moisture content cannot be directly used in permafrost areas, because it does not consider the bias introduced by the seasonal surface elevation changes due to active layer thawing. We propose a solution to improve this default method by introducing modeled surface elevation changes. We validate this modified method using the GPS data and in situ observations at a permafrost site in the northeastern Qinghai–Tibet Plateau (QTP). The root-mean-square error and correlation coefficient between the GPS-IR estimates of soil moisture content and the in situ ones improve from 1.85 % to 1.51 % and 0.71 to 0.82, respectively. We also propose a framework to integrate the GPS-IR estimates of these three variables at one site and illustrate it using the same site in the QTP as an example. This study highlights the improvement to the default algorithm, which makes the GPS-IR valid in estimating soil moisture content in permafrost areas. The three-in-one framework is able to fully utilize the GPS-IR in permafrost areas and can be extended to other sites such as those in the Arctic. This study is also the first to use GPS-IR to estimate environmental variables in the QTP, which fills a spatial gap and provides complementary measurements to ground temperature and active layer thickness.

scholarly journals Three-in-one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai-Tibet Plateau

2020 ◽  
Author(s):  
Jiahua Zhang ◽  
Lin Liu ◽  
Lei Su ◽  
Tao Che
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Active Layer ◽  
Snow Depth ◽  
Soil Moisture Content ◽  
Ground Surface ◽  
Surface Elevation ◽  
Tibet Plateau ◽  
Elevation Changes ◽  
Qinghai Tibet Plateau

Abstract. Ground surface elevation changes, soil moisture, and snow depth are all essential variables for studying the dynamics of the active layer and permafrost. GPS interferometric reflectometry (GPS-IR) has been used to measure surface elevation changes and snow depth in permafrost areas. However, its applicability to estimating soil moisture in permafrost regions has not been assessed. Moreover, these variables were usually measured separately at different sites. Integrating their estimates at one site facilitates the comprehensive utilization of GPS-IR in permafrost studies. In this study, we run simulations to elucidate that the commonly-used GPS-IR method for estimating soil moisture content cannot be directly used in permafrost areas, because it does not consider the bias introduced by the seasonal surface elevation changes due to thawing of the active layer. We propose a solution to improve this default method by introducing modeled surface elevation changes. We validate this modified method using the GPS data and in situ observations at a permafrost site in the northeastern Qinghai-Tibet Plateau (QTP). The root-mean-square error and correlation coefficient between the GPS-IR estimates of soil moisture content and the in situ ones improve from 1.85 % to 1.51 % and 0.71 to 0.82, respectively. We also implement a framework to integrate the GPS-IR estimates of these three variables at one site and illustrate it using the same site in the QTP as an example. This study highlights the improvement to the default method, which makes the GPS-IR valid in estimating soil moisture content in permafrost areas. The three-in-one framework is able to fully utilize the GPS-IR in permafrost areas and can be extended to other sites such as those in the Arctic. This study is also the first to use GPS-IR to estimate environmental variables in the QTP, which fills a spatial gap and provides complementary measurements to those of ground temperature and active layer thickness.

Assessment of reanalysis soil moisture products in the permafrost regions of the central of the Qinghai-Tibet Plateau

2017 ◽  
Vol 31 (26) ◽  
pp. 4647-4659 ◽  
Author(s):  
Yanhui Qin ◽  
Tonghua Wu ◽  
Xiaodong Wu ◽  
Ren Li ◽  
Changwei Xie ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Tibet Plateau ◽  
Qinghai Tibet Plateau ◽  
Permafrost Regions
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